1. Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02 Abduction Using Neural Models by Madan Bharadwaj Instructor: Dr.Avelino Gonzalez

2. Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02 Agenda Introduce the Concept Why Neural Approach ? UNIFY Hopfield Model Critique Summary

3. Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02 Abduction & NN’s What are Neural Networks? What is Abduction?

4. Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02 The Analogy Figure 1: Handwritten Characters. A’s and B’s Figure 2: After training the Neural Network classifies data into classes

5. Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02 Major References “A Unified Model for Abduction- Based Reasoning” by Ayeb et al “A Neural Architecture for a Class of Abduction Problems” by Goel et al

6. Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02 Types of Abd. Problems 4 Major Types Open & Incompatible Classes

7. Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02 UNIFY NN Architecture reflects problem dynamics Tackles all 4 classes Architecture incrementally introduced Simple Architecture

8. Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02 UNIFY - Initial Model Inhibitory Weights Excitatory Weights Hypothesis Layer Observation Layer

9. Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02 The Algorithm Initialize cells and weights Update cells and weights Check Termination condition

10. Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02 UNIFIED MODEL Intermediate Layer

11. Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02 Modifications Incompatibility Weights Modified Equations

12. Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02 Experiments Toy Problems Real Life Problem Results very encouraging

13. Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02 Hopfield Model Energy Function approach Only linear and monotonic classes Partition data into sub domains Map sub domains Minimize Energy Function ART Model also proposed

14. Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02 Critique Fuzzy Framework essential for abduction Neural Networks still abstract

15. Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02 Future Avenues Cancellation Class Better designs using ART Evolving Architectures Other Approaches

16. Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02 Summary Neural Network Approach feasible UNIFY is better Vast scope for further research

17. Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02 References [1]. B.Ayeb, S.Wang and J.Ge, “A Unified Model for Abduction-Based Reasoning” IEEE Transaction on Systems, Man and Cybernetics – Part A: Systems and Humans, Vol 28, No. 4, July 1998 [2]. A.K. Goel and J. Ramanujam, “A Neural Architecture for a Class of Abduction Problems”, IEEE Transaction on Systems, Man and Cybernetics – Part B – Cybernetics, Vol. 26, No. 6, December 1996 [3]. _____, “A Connectionist Model for Diagnostic Problem Solving: Part II”, IEEE Transaction on Systems, Man and Cybernetics., Vol19, pp. 285-289, 1989 [4]. A. Goel, J. Ramanujam and P. Sadayappan, “Towards a ‘neural’ architecture of abductive reasoning”, in Proc. 2 nd Int. Conf. Neural Networks, 1988, pp. I-681-I- 688. [5]. D.Poole, A. Mackworth and R.Goebel, “Computational Intelligence: A Logical Approach”, pp 319-343, Oxford University Press, 1998. [6]. C. Christodoulou and M. Georgiopoulos, “Applications of Neural Networks in Electromagnetics”, Boston: Artech House, 2001. [7]. Castro, J.L.; Mantas, C.J.; Benitez, J.M., “Interpretation of artificial neural networks by means of fuzzy rules”, IEEE Transactions on Neural Networks, Volume: 13 Issue: 1, Jan. 2002. Page(s): 101 –116 [8]. T. Bylander, D. Allemang, M. C. Tanner, and J. R. Josephon, “The computational complexity of abduction,” Artif. Intell., vol. 49, pp. 25–60, 1991.

18. Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02 A n y Q u e s t i o n s...